Why Choose AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting Training Course?
Reservoir models and production forecasts guide decisions throughout the life of an oil and gas asset. They influence development plans, well placement, production targets, reserves estimates and investment priorities. Yet subsurface information is incomplete, reservoir behaviour is uncertain and production data can be affected by changing operating conditions. These challenges make forecasting a continuing engineering task, rather than a one-time calculation.
AI and machine learning can help upstream teams analyse large volumes of geological, petrophysical, well and production data. They can reveal patterns, generate alternative forecasts and support faster evaluation of development scenarios. Their usefulness depends on the quality and relevance of the input data, appropriate validation and a sound understanding of reservoir physics. A model that fits historical production closely may still perform poorly when operating conditions change or when it is applied to a new well.
This AI for Upstream Oil & Gas: Reservoir Modelling and Production Forecasting training course examines how AI can complement established reservoir engineering and production forecasting methods. Participants follow the workflow from data preparation and feature selection to model development, uncertainty assessment and decision support. The course emphasises engineering interpretation, comparison with conventional methods and clear communication of forecast assumptions and limitations.
What are the Goals?
By the end of this course, participants will be able to:
- Identify upstream applications where AI can support reservoir modelling and forecasting.
- Prepare and assess subsurface, well and production data for analysis.
- Select relevant geological, reservoir and operating variables.
- Compare conventional forecasting approaches with AI-based methods.
- Develop and evaluate models for well and field production forecasting.
- Recognise data leakage, overfitting and other sources of misleading results.
- Assess uncertainty and communicate forecast ranges.
- Combine AI outputs with reservoir engineering knowledge and physical constraints.
- Define an implementation plan for an upstream AI use case.
Who is this Training Course for?
This training course is suitable to a wide range of professionals but will greatly benefit:
- Reservoir and petroleum engineers
- Production and well performance engineers
- Geoscientists and petrophysicists
- Field development and asset planning professionals
- Upstream data analysts and data scientists
- Digital transformation teams in oil and gas
- Technical managers responsible for production forecasts
How will this Training Course be Presented?
The course combines technical presentations, upstream case studies, group discussions and guided data exercises. Participants examine a representative reservoir and production dataset, compare forecasting approaches, assess model performance and present a recommendation for an asset decision. No advanced programming experience is required to follow the course concepts.
The Course Content
- Reservoir modelling and production forecasting decisions across the asset lifecycle
- Conventional reservoir engineering and forecasting approaches
- AI and machine learning applications in upstream operations
- Sources of geological, petrophysical, well and production data
- Data quality, missing values and inconsistent reporting
- Aligning production data with well events and operating conditions
- Defining the forecast target, time horizon and decision context
- Selecting an upstream use case for analysis
- Integrating static reservoir and dynamic production data
- Selecting features related to rock, fluid and well performance
- Analysing pressure, rates, water cut and gas–oil ratio trends
- Accounting for shut-ins, workovers and artificial lift changes
- Identifying outliers and separating errors from significant events
- Segmenting wells and reservoirs with comparable characteristics
- Exploring relationships between inputs and production outcomes
- Documenting data assumptions and limitations
- Using AI to support reservoir characterisation
- Predicting reservoir properties from available measurements
- Identifying patterns across wells and geological zones
- Developing proxy models for rapid scenario evaluation
- Comparing AI predictions with geological and engineering understanding
- Incorporating physical constraints into model evaluation
- Validating results where subsurface observations are limited
- Interpreting model outputs for field development decisions
- Establishing decline curve and engineering forecast benchmarks
- Preparing time-series data for well and field forecasts
- Comparing machine learning approaches for production prediction
- Defining training, validation and test periods
- Preventing data leakage and unrealistic forecast accuracy
- Forecasting under changing operating conditions
- Evaluating errors across wells, time horizons and production levels
- Comparing AI forecasts with conventional methods
- Identifying geological, operational and model uncertainty
- Developing forecast ranges and alternative production scenarios
- Stress-testing forecasts against changing assumptions
- Explaining model results to engineering and asset teams
- Integrating forecasts into reservoir surveillance and planning
- Monitoring performance and updating models as new data arrives
- Presenting an AI-supported asset forecasting case
- Developing a phased implementation roadmap for an upstream team
Certificate
- AZTech Certificate of Completion for delegates who attend and complete the training course
Do you want to learn more about this course?
Register now or contact our team to discuss schedules, delivery formats, and customised options.